most citedFully trainable Gaussian derivative convolutional layer

1 citations · 1 across the 5 of their papers we have counts for

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5 papers

eess.IV2022

Differential invariants for SE(2)-equivariant networks

Mateus Sangalli, Samy Blusseau, Santiago Velasco-Forero +1

Symmetry is present in many tasks in computer vision, where the same class of objects can appear transformed, e.g. rotated due to different camera orientations, or scaled due to pe…

cs.NE20221 cited

Fully trainable Gaussian derivative convolutional layer

Valentin Penaud--Polge, Santiago Velasco-Forero, Jesus Angulo

The Gaussian kernel and its derivatives have already been employed for Convolutional Neural Networks in several previous works. Most of these papers proposed to compute filters by…

cs.LG2022

MorphoActivation: Generalizing ReLU activation function by mathematical morphology

Santiago Velasco-Forero, Jesús Angulo

This paper analyses both nonlinear activation functions and spatial max-pooling for Deep Convolutional Neural Networks (DCNNs) by means of the algebraic basis of mathematical morph…

eess.SP2022

Morphological adjunctions represented by matrices in max-plus algebra for signal and image processing

Samy Blusseau, Santiago Velasco-Forero, Jesus Angulo +1

In discrete signal and image processing, many dilations and erosions can be written as the max-plus and min-plus product of a matrix on a vector. Previous studies considered operat…

cs.CV2016

Automatic Selection of Stochastic Watershed Hierarchies

Amin Fehri, Santiago Velasco-Forero, Fernand Meyer

The segmentation, seen as the association of a partition with an image, is a difficult task. It can be decomposed in two steps: at first, a family of contours associated with a ser…